django-celery-expert

Expert guidance for Django Celery task design, configuration, error handling, and production monitoring.

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/vintasoftware/django-ai-plugins --skill django-celery-expert

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this week

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What it does

  • Covers task design patterns, Django ORM integration, transaction safety, and idempotency best practices

  • Includes configuration for brokers, result backends, worker settings, queue routing, and task serialization

  • Provides error handling strategies: retries with exponential backoff, dead letter queues, timeouts, and exception logging

  • Supports periodic task scheduling with Celery

Category

Backend

Last updated

Apr 8, 2026

Installation Guide

How to use django-celery-expert on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add django-celery-expert
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/vintasoftware/django-ai-plugins --skill django-celery-expert

Fetches django-celery-expert from vintasoftware/django-ai-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/django-celery-expert

Restart Cursor to activate django-celery-expert. Access via /django-celery-expert in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Django Celery Expert

Instructions

Step 1: Classify the Request

Identify the task category from the request:

  • Django integration — transaction safety, ORM patterns, testing, request correlation → read references/django-integration.md
  • Task design — new tasks, calling patterns, chains/groups/chords, idempotency → read references/task-design-patterns.md
  • Configuration — broker setup, result backend, worker settings, queue routing → read references/configuration-guide.md
  • Error handling — retries, backoff, dead letter queues, timeouts → read references/error-handling.md
  • Periodic tasks — Celery Beat, crontab schedules, dynamic schedules, timezone handling → read references/periodic-tasks.md
  • Monitoring — Flower, Prometheus, logging, debugging stuck tasks → read references/monitoring-observability.md
  • Production deployment — scaling, supervision, containers, health checks → read references/production-deployment.md

If the request spans multiple categories, read all relevant reference files before continuing.

Step 2: Read the Reference File(s)

Read each reference file identified in Step 1. Do not proceed to implementation without reading the relevant reference.

Step 3: Implement

Apply the patterns from the reference file. Before presenting the solution, verify:

  • Task arguments are serializable (pass IDs, not model instances)
  • Tasks with retries enabled are idempotent
  • Errors are logged with context
  • Long-running tasks have timeouts configured

Examples

Basic Background Task

Request: "Send welcome emails in the background after user registration"

# tasks.py
from celery import shared_task
from django.core.mail import send_mail

@shared_task(bind=True, max_retries=3)
def send_welcome_email(self, user_id):
    from users.models import User

    try:
        user = User.objects.get(id=user_id)
        send_mail(
            subject="Welcome!",
            message=f"Hi {user.name}, welcome to our platform!",
            from_email="[email protected]",
            recipient_list=[user.email],
        )
    except User.DoesNotExist:
        pass
    except Exception as exc:
        raise self.retry(exc=exc, countdown=60 * (2 ** self.request.retries))


# views.py — queue only after the transaction commits
from django.db import transaction

def register(request):
    user = User.objects.create(...)
    transaction.on_commit(lambda: send_welcome_email.delay(user.id))
    return redirect("dashboard")

Task with Progress Tracking

Request: "Process a large CSV import with progress updates"

@shared_task(bind=True)
def import_csv(self, file_path, total_rows):
    from myapp.models import Record

    with open(file_path) as f:
        reader = csv.DictReader(f)
        for i, row in enumerate(reader):
            Record.objects.create(**row)
            if i % 100 == 0:
                self.update_state(
                    state="PROGRESS",
                    meta={"current": i, "total": total_rows},
                )

    return {"status": "complete", "processed": total_rows}


# Poll progress
result = import_csv.AsyncResult(task_id)
if result.state == "PROGRESS":
    progress = result.info.get("current", 0) / result.info.get("total", 1)

Workflow with Chains

Request: "Process an order: validate inventory, charge payment, then send confirmation"

from celery import chain

@shared_task
def validate_inventory(order_id):
    order = Order.objects.get(id=order_id)
    if not order.items_in_stock():
        raise ValueError("Items out of stock")
    return order_id

@shared_task
def charge_payment(order_id):
    order = Order.objects.get(id=order_id)
    order.charge()
    return order_id

@shared_task
def send_confirmation(order_id):
    Order.objects.get(id=order_id).send_confirmation_email()

def process_order(order_id):
    chain(
        validate_inventory.s(order_id),
        charge_payment.s(),
        send_confirmation.s(),
    ).delay()

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.749 reviews
  • K
    Kofi GonzalezDec 20, 2024

    django-celery-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • M
    Meera KapoorDec 16, 2024

    Keeps context tight: django-celery-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • C
    Chaitanya PatilDec 12, 2024

    Useful defaults in django-celery-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • M
    Maya WangDec 12, 2024

    django-celery-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • H
    Henry LopezDec 8, 2024

    I recommend django-celery-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • H
    Henry HaddadNov 27, 2024

    Solid pick for teams standardizing on skills: django-celery-expert is focused, and the summary matches what you get after install.

  • H
    Henry RahmanNov 11, 2024

    django-celery-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • L
    Layla RaoNov 7, 2024

    We added django-celery-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • P
    Piyush GNov 3, 2024

    django-celery-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • I
    Ishan KhannaNov 3, 2024

    django-celery-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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